Logistics & Supply Chain · Last-mile optimization
Your dispatchers re-plan routes all day and trucks still miss windows
Banao builds last-mile optimization that assigns, sequences, and re-routes your fleet against live traffic, delivery windows, and vehicle capacity — without a dispatcher rebuilding a spreadsheet every two hours.
The system runs against real order data and TMS records, produces a driveable plan in under a minute, and keeps a human in the loop for overrides. Swiggy runs a variant of this pattern across its live delivery network.
Swiggy— dispatch and last-mile routing models running against live order and traffic data.
The first call is free · 45 minutes · no obligation
What we build
What a Banao last-mile optimization deployment covers
Route optimization is one problem with five dependencies — constraint handling, re-routing, driver UX, TMS integration, and dispatcher override. We ship all of them.
Multi-constraint route planning
Orders, vehicle capacities, driver shift limits, delivery windows, and restricted zones all feed the optimizer. Plans respect every constraint before a truck moves.
Live re-routing on disruption
When a driver is delayed, a window is lost, or a new order drops in, the model re-sequences the affected vehicles automatically and pushes the updated plan to the driver app.
Driver app with step-by-step guidance
Drivers receive a sequenced task list with navigation, proof-of-delivery capture, and an exception button — not a phone call from dispatch every time something changes.
Dispatcher dashboard with override
Dispatchers see every vehicle's position and plan in one view, can reassign stops, and approve or reject model suggestions. The AI handles the computation; humans handle the judgement calls.
TMS and order-management integration
Route plans feed back into your existing TMS or order-management system — no parallel tracking system, no double entry, no manual export step.
On-time rate and cost reporting
Per-route, per-driver, and per-zone reporting on delivery performance, fuel consumption, and window adherence, so operations heads can see where the margin is going.
Receipts
Where this pattern runs today
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified against the operator's data.
Dispatch AI across a live hyperlocal delivery network
Banao built and deployed routing and dispatch optimization that handles multi-drop sequencing against live traffic and delivery windows across Swiggy's network. The dispatcher-override layer was a non-negotiable requirement from day one.
Dogfooding
We run the same operational discipline ourselves
Banao manages a ~300-person engineering operation, and every AI system we sell runs inside our own business first. InterviewGod screens our own engineering hires; Vikaas runs our own demand-generation pipeline. A system that has to survive inside a real operation is already tested before it touches yours.
When we say a last-mile model needs a human override layer and real TMS integration to survive beyond a pilot, that is not theory. We know what happens when a system is deployed without it.
Screens Banao's own engineering hires before every cohort intake.
Runs Banao's own demand-gen pipeline end to end, every week.
The honest version
When last-mile AI is not the right investment
Route optimization compounds on volume and constraint complexity. Below certain thresholds, simpler tools outperform a custom AI deployment:
- Low drop density: fewer than around 50 drops per vehicle per day across a small geography often runs fine on a commercial SaaS routing tool. We will say so rather than overbuild.
- Unstable network: if your delivery zones, vehicles, or product mix change monthly, the model's constraint definitions rot faster than the gain. Fix the operational foundations first.
- No TMS or order data: the optimizer is only as good as its inputs. If orders live in WhatsApp threads and vehicle availability is tracked in a notebook, the integration effort is the real project — and may not need AI at all.
How we start
How we start — prove it on one delivery zone first
We do not quote a fleet-wide optimization system off a discovery call. We look at your actual routes, windows, and dispatch data first.
- 01
AI Discovery Sprint
2 weeks · fixed price
We audit a sample of your real route and order data, model one delivery zone, and hand back a baseline plan-quality estimate and ROI calculation — yours to keep regardless of next steps. If you proceed to build, the Sprint fee is credited against the engagement.
- 02
Pilot build
We build the optimizer, driver app, and dispatcher dashboard for a defined zone or vehicle class. TMS integration and dispatcher override are part of the deliverable, not add-ons.
- 03
Fleet-wide rollout
Phased expansion to the full network with monitoring, re-routing logic tuned to your lanes, and a performance dashboard operations heads check daily.
FAQ
Frequently asked questions
How long does it take to get a route plan from the model?
For most fleet sizes, the optimizer produces a driveable plan in under 60 seconds after order cut-off. Re-routing on a mid-day disruption is typically faster — it only re-sequences the affected vehicles, not the whole fleet.
Can dispatchers override the AI plan?
Yes, and deliberately so. The dispatcher dashboard shows the AI plan, lets dispatchers reassign stops or swap vehicles, and logs the override reason. We do not ship a system that removes dispatcher judgement — that is how pilots fail in month three.
Does this work with our existing TMS?
We integrate with TMS platforms via API or direct database connection. Where a TMS exposes no API, we build a middleware layer. The Discovery Sprint includes a TMS integration assessment, so you know the effort before committing to the build.
What if our network is hyperlocal versus long-haul?
The optimization model differs: hyperlocal (sub-30-minute, high drop density) uses different constraint weighting than intercity or long-haul routes. We scope the right solver for your network in the Discovery Sprint rather than applying a one-size-fits-all engine.
How do you handle driver app adoption?
Driver onboarding and change management are part of the rollout deliverable, not an afterthought. The app interface is kept to the minimum a driver needs mid-route — sequence, navigation, and a single exception button — to reduce friction. Adoption metrics are tracked from day one.
Get started
Show us your worst delivery day
Bring your last month of route and order data. In 45 minutes we will tell you whether an optimization model would move your on-time rate, and what a realistic build would require.
Book a Discovery Sprint